PROJECT SUMMARY The authors have developed a computational platform to rapidly identify optimal drug and dose combinations from the innumerable possibilities. By testing this technique termed Phenotypic Personalized Medicine (PPM) in a diverse number of experimental systems representing different diseases, they have found that the response of biological systems to drugs can be described by a low order, smooth multidimensional surface. The main consequence of this is that optimal drug combinations can be found in a small number of tests. This input?output relationship is always based on experimental data, not modeling, and it would lead to a straightforward solution for handling human diversity in drug dosing needs, among other clinical problems. They will test the hypothesis that PPM can be developed and validated for clinical use by conducting a prospective clinical trial to compare the feasibility and efficacy of this approach to standard of care physician dosing. This group has previously used PPM-based optimization to find novel drug combinations in in vitro and in vivo models of cancer and infection. In a first-in-human study, they recently compared 4 PPM-dosed patients and 4 control (standard of care dosed) patients. They calculated the tacrolimus dosing regimen using the PPM process and showed significant improvement in variability and a trend toward improved efficacy. For this application, they aim to show in a clinical trial, that PPM is more effective than unaided physician dosing. This will allow the generation of data to justify a multi-center confirmatory study and to explore a wider array of clinical outcomes to optimize.